Agent Trust Fabric
The product bet
Enterprise buyers do not want a pile of agent safety checklists. They want one answer before an agent touches tools or private context:
Can we trust this agent, for this workflow, in this tenant, right now?
The Agent Trust Fabric composes six dimensions into that answer:
- Identity - who the agent is, which delegated identity it is using, and whether that identity still has valid scope.
- Context - what the agent is consuming, whether the context is fresh, and whether poisoning signals were found.
- Scope - where the agent can go and which action class is being attempted.
- Behavior - what the agent is doing in real time and whether telemetry can reconstruct the run.
- Data boundary - what the agent is sending or serving outside the trust boundary.
- Containment - whether kill switches, SOC detections, and hosted MCP controls can stop the run when prevention fails.
That is the acquisition-grade wedge: open knowledge creates adoption; the production MCP server becomes the control point that signs trust verdicts for customer-private agent runs.
What was added
data/assurance/agent-trust-fabric-profile.json- source contract for trust dimensions, tiers, runtime fields, source references, and commercialization path.scripts/generate_agent_trust_fabric_pack.py- deterministic generator and--checkvalidator.scripts/evaluate_agent_trust_fabric_decision.py- deterministic allow, hold, deny, or kill evaluator.data/evidence/agent-trust-fabric-pack.json- generated MCP-readable trust fabric evidence.recipes_agent_trust_fabric_pack- MCP lookup by dimension, workflow, trust tier, or status.recipes_evaluate_agent_trust_fabric_decision- MCP runtime evaluator for one proposed agent run.
Run it from the repo root:
python3 scripts/generate_agent_trust_fabric_pack.py
python3 scripts/generate_agent_trust_fabric_pack.py --checkEvaluate a trusted scoped run:
python3 scripts/evaluate_agent_trust_fabric_decision.py \
--workflow-id vulnerable-dependency-remediation \
--run-id run-trust \
--agent-id sr-agent::vulnerable-dependency-remediation::codex \
--identity-id sr-agent::vulnerable-dependency-remediation::codex \
--tenant-id tenant-demo \
--correlation-id corr-trust \
--trust-event-id trust-evt-1 \
--requested-trust-tier operator \
--intent-summary "Patch dependency lockfiles on a scoped remediation branch" \
--context-package-hash sha256:context \
--policy-pack-hash sha256:policy \
--authorization-decision allow_authorized_mcp_request \
--egress-decision allow_internal_context \
--action-runtime-decision allow_bounded_action \
--telemetry-decision telemetry_ready \
--soc-decision no_alert \
--telemetry-event-id trace-1 \
--receipt-id receipt-1 \
--source-freshness-decision current \
--approval-id approval-1 \
--approval-status approved \
--expect-decision allow_trusted_agent_contextEvaluate a trust break:
python3 scripts/evaluate_agent_trust_fabric_decision.py \
--workflow-id sensitive-data-remediation \
--run-id run-kill \
--agent-id sr-agent::sensitive-data-remediation::codex \
--identity-id sr-agent::sensitive-data-remediation::codex \
--tenant-id tenant-demo \
--correlation-id corr-kill \
--trust-event-id trust-evt-2 \
--requested-trust-tier operator \
--intent-summary "Investigate a possible token in logs" \
--context-package-hash sha256:context \
--policy-pack-hash sha256:policy \
--authorization-decision allow_authorized_mcp_request \
--egress-decision hold_for_redaction_or_dpa \
--telemetry-decision telemetry_ready \
--telemetry-event-id trace-2 \
--receipt-id receipt-2 \
--source-freshness-decision current \
--token-passthrough \
--expect-decision kill_session_on_agent_trust_breakWhat is inside
| Section | Purpose |
|---|---|
trust_fabric_summary | Dimension count, workflow count, trust-tier distribution, source pack count, and failure count. |
trust_contract | Default fail-closed state, required runtime fields, score thresholds, evidence sources, and kill signals. |
trust_dimensions | The identity, context, scope, behavior, data-boundary, and containment checks, with MCP tools and evidence paths. |
trust_tiers | Intern, Apprentice, Operator, and Principal tiers with score gates and allowed actions. |
workflow_trust_matrix | Generated default trust tier and risk flags for every active workflow. |
tabletop_cases | Ready-made allow, hold, deny, and kill cases for platform testing. |
source_artifacts | Hashes and paths for each evidence pack used to build the trust fabric. |
MCP examples
Get the executive summary and trust matrix:
{}Inspect one dimension:
{
"dimension_id": "identity"
}Inspect a workflow:
{
"workflow_id": "vulnerable-dependency-remediation"
}Find workflows that default to Operator:
{
"trust_tier": "operator"
}Evaluate one agent trust request:
{
"workflow_id": "vulnerable-dependency-remediation",
"run_id": "run-123",
"agent_id": "sr-agent::vulnerable-dependency-remediation::codex",
"identity_id": "sr-agent::vulnerable-dependency-remediation::codex",
"tenant_id": "tenant-a",
"correlation_id": "corr-123",
"trust_event_id": "trust-123",
"requested_trust_tier": "operator",
"intent_summary": "Patch dependency lockfiles on a scoped remediation branch.",
"context_package_hash": "sha256:context",
"policy_pack_hash": "sha256:policy",
"authorization_decision": "allow_authorized_mcp_request",
"egress_decision": "allow_internal_context",
"action_runtime_decision": "allow_bounded_action",
"telemetry_decision": "telemetry_ready",
"soc_decision": "no_alert",
"telemetry_event_id": "trace-123",
"receipt_id": "receipt-123",
"source_freshness_decision": "current",
"human_approval_record": {
"approval_id": "approval-123",
"status": "approved"
}
}Why it is acquisition-grade
The trust fabric makes the site easier to understand and easier to sell. It creates one cross-vendor primitive that xAI, Anthropic, OpenAI, an AI platform vendor, or a security vendor could attach to agent hosts, MCP gateways, customer telemetry, and trust-center exports:
- hosted trust scoring APIs,
- signed agent trust verdicts,
- customer policy adapters,
- SOC/SIEM export,
- procurement evidence export,
- runtime step-up and kill decisions,
- trust-center proof for design partners,
- a direct bridge from open knowledge to production MCP enforcement.
Industry alignment
The pack is anchored in current primary guidance:
- CSA ATF: Zero Trust for AI Agents for identity, behavior, data governance, least privilege, and containment.
- NIST AI Agent Standards Initiative for trusted, interoperable, secure agent standards.
- NIST CAISI AI Agent Security RFI for indirect prompt injection, poisoning, misaligned actions, and constrained deployment access.
- MCP Authorization 2025-11-25 for protected-resource metadata, scope challenges, resource indicators, token audience validation, and token-passthrough denial.
- OWASP MCP Top 10 for token exposure, scope creep, tool poisoning, command execution, insufficient auth, shadow MCP servers, and over-sharing.
- OpenAI Agents SDK Guardrails for guardrail placement around first input, final output, and function-tool calls.
- OpenTelemetry GenAI agent spans for portable agent and tool execution traces.